#financial-forecasting News & Analysis

6 articles tagged with #financial-forecasting. AI-curated summaries with sentiment analysis and key takeaways from 50+ sources.

6 articles

AI × CryptoBullisharXiv – CS AI · Jun 37/10

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From Long News to Accurate Forecast: Importance-Aware Fusion and PRM-Guided Reflection for Time Series Forecasting

Researchers propose a novel framework combining importance-aware news compression and process reward models to improve LLM-based time series forecasting across finance, energy, and cryptocurrency markets. The method addresses practical limitations of existing approaches by intelligently filtering news articles within context windows and guiding iterative retrieval, achieving better accuracy with fewer refinement iterations.

$BTC

AIBullisharXiv – CS AI · Mar 177/10

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EARCP: Self-Regulating Coherence-Aware Ensemble Architecture for Sequential Decision Making -- Ensemble Auto-Regule par Coherence et Performance

Researchers introduce EARCP, a new ensemble architecture for AI that dynamically weights different expert models based on performance and coherence. The system provides theoretical guarantees with sublinear regret bounds and has been tested on time series forecasting, activity recognition, and financial prediction tasks.

AIBullisharXiv – CS AI · Mar 117/10

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A Hybrid Quantum-Classical Framework for Financial Volatility Forecasting Based on Quantum Circuit Born Machines

Researchers developed a hybrid quantum-classical framework combining LSTM neural networks with Quantum Circuit Born Machines for financial volatility forecasting. Testing on Shanghai Stock Exchange data showed significant improvements over classical methods in key metrics like MSE and RMSE, demonstrating quantum computing's potential in financial modeling.

AINeutralarXiv – CS AI · Jun 26/10

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MOSAIC: Modular Orchestration for Structured Agentic Intelligence and Composition

Researchers introduce MOSAIC, a structured agentic framework that automates data science model selection by combining LLM flexibility with systematic verification. Unlike traditional AutoML systems or unstructured LLM agents, MOSAIC creates intermediate 'blueprints' that ground decisions in retrieved evidence and execution feedback, improving task performance and decision traceability.

AINeutralarXiv – CS AI · May 286/10

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GS-FUSE: Granger-Supervised Gated Fusion and Multi-Granularity Alignment for Event-Driven Financial Forecasting

Researchers introduce GS-Fuse, a machine learning framework that improves financial forecasting by intelligently combining event-driven text with price data. The system uses causal analysis to determine when news actually predicts market movements, addressing a key limitation in existing multimodal AI models that treat all data sources equally.

AIBullisharXiv – CS AI · May 16/10

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CastFlow: Learning Role-Specialized Agentic Workflows for Time Series Forecasting

Researchers introduce CastFlow, a dynamic agentic framework that applies large language models to time series forecasting through multi-stage workflows combining planning, action, and reflection. The system uses role-specialized agents—a general-purpose LLM paired with a fine-tuned domain-specific model—to iteratively refine forecasts using ensemble methods and contextual memory, demonstrating superior performance over existing static generative approaches.